The normal-tangent-G class of probabilistic distributions: properties and real data modelling

This paper introduces a novel class of probability distributions called nonnal-tangent-G, whose submodels are parsimonious and bring no additional parameters besides the baseline's. We demonstrate that these submodels are identifiable as long as the baseline is. We present some properties of th...

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Bibliographic Details
Published in:Pakistan journal of statistics and operation research 2020-10, Vol.16 (4), p.827-838
Main Authors: Silveira, Fábio V J, Gomes-Silva, Frank, Brito, Cicero C R, Cunha-Filho, Moacyr, Jale, Jader S, Gusmão, Felipe, Xavier-Júnior, Sílvio
Format: Article
Language:English
Subjects:
Informatics
Mathematical models
Maximum likelihood estimates
Modelling
Monte Carlo simulation
Parameter identification
Probability density functions
Random variables
Skewness
Statistical analysis
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Summary:This paper introduces a novel class of probability distributions called nonnal-tangent-G, whose submodels are parsimonious and bring no additional parameters besides the baseline's. We demonstrate that these submodels are identifiable as long as the baseline is. We present some properties of the class, including the series representation of its probability density function (pdf) and two special cases. Monte Carlo simulations are carried out to study the behavior of the maximum likelihood estimates (MLEs) of the parameters for a particular submodel. We also perfonn an application of it to a real dataset to exemplify the modelling benefits of the class.
ISSN:1816-2711
2220-5810
DOI:10.18187/pjsor.v1614.3443